YCBInEOAT dataset [1]}: This dataset helps verify the effectiveness of 6D pose tracking during robot manipulation. It was originally developed to evaluate approaches relying on CAD models. The available CAD models, however, are not used by BundleTrack. In contrast to the NOCS dataset where objects are statically placed on a tabletop and captured by a moving camera, YCBInEOAT contains 9 video sequences captured by a static RGB-D camera, while objects are dynamically manipulated. There are three types of manipulation: (1) single arm pick-and-place, (2) within-hand manipulation, and (3) pick to hand-off between arms to placement. These scenarios and the end-effectors used make directly computing poses from forward kinematics unreliable. The manipulation videos involve 5 YCB Objects [2]}: mustard bottle, tomato soup can, sugar box, bleach cleanser and cracker box.
